2021/08/09 by Jonas le Fevre Sejersen, Sejersen, Jonas le Fevre, Rui Pimentel de Figueiredo +3
Arts and Humanities · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Maritime Navigation and Safety #Maritime and Coastal Archaeology #Robotics (cs.RO) #Underwater Vehicles and Communication Systems
paper · pdf · doi:10.48550/arxiv.2108.03862
openalex publication_date 2021/08/09 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
In the maritime sector, safe vessel navigation is of great importance, particularly in congested harbors and waterways. The focus of this work is to estimate the distance between an object of interest and potential obstacles using a companion UAV. The proposed approach fuses GPS data with long-range aerial images. First, we employ semantic segmentation DNN for discriminating the vessel of interest, water, and potential solid objects using raw image data. The network is trained with both real and images generated and automatically labeled from a realistic AirSim simulation environment. Then, the distances between the extracted vessel and non-water obstacle blobs are computed using a novel GSD estimation algorithm. To the best of our knowledge, this work is the first attempt to detect and estimate distances to unknown objects from long-range visual data captured with conventional RGB cameras and auxiliary absolute positioning systems (e.g. GPS). The simulation results illustrate the accuracy and efficacy of the proposed method for visually aided navigation of vessels assisted by UAV.